This repository contains a PyTorch implementation of the Pose Residual Network (PRN) presented in our ECCV 2018 paper:
Muhammed Kocabas, Salih Karagoz, Emre Akbas. MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network. In ECCV, 2018. arxiv
PRN is described in Section 3.2 of the paper.
We have tested our method on Coco Dataset
python
pytorch
numpy
tqdm
pycocotools
progress
scikit-image
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Clone this repository
git clone https://github.com/salihkaragoz/pose-residual-network-pytorch.git -
Install Pytorch
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pip install -r src/requirements.txt -
To download COCO dataset train2017 and val2017 annotations run:
bash data/coco.sh. (data size: ~240Mb)
python train.py
For more options look at opt.py
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Download pre-train model
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python test.py --test_cp=PathToPreTrainModel/PRN.pth.tar
Results on COCO val2017 Ground Truth data.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.892
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.978
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.921
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.883
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.912
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.917
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.982
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.937
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.902
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.944
If you find this code useful for your research, please consider citing our paper:
@Inproceedings{kocabas18prn,
Title = {Multi{P}ose{N}et: Fast Multi-Person Pose Estimation using Pose Residual Network},
Author = {Kocabas, Muhammed and Karagoz, Salih and Akbas, Emre},
Booktitle = {European Conference on Computer Vision (ECCV)},
Year = {2018}
}